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Recognition of Mathematics Notation Via Computer Using Baseline

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Recognition of Mathematics Notation via Computer Using Baseline Structure Richard Zanibbi zanibbicsqueensuca August External Technical Rep ort ISSN Department of Computing and Information Science Queens University Kingston Ontario Canada KL N Do cument prepared August c Copyright Richard Zanibbi Abstract The spatial structure of mathematical expressions may b e represented using the structure of baselines in an expression This baseline structure may b e represented as a tree and then rewritten to represent dierent interpretations of spatial structure in an expression This structure tree may also b e trans A lated into mathematical string languages such as L T X Maple etc A parsing E algorithm which extracts baseline structure in mathematics expressions from a list of attributed symb ols is presented along with an implementation of the algorithm The implementation of the parsing algorithm has b een in terfaced with an online mathematics entry system The resulting integrated system allowed testing of the parser on hundreds of input expressions with encouraging results Future work in the extension of the current mathematics recognition technique and application of directionbased recognition to other notations such as music notation and circuit diagrams are also discussed Acknowledgments Thanks to Dorothea Blostein who was a fun knowledgeable and supp ortive sup ervisor Ed Lank for all his help with the design of DRACULAE and the ideas in this thesis Nick Willan for his implementation of the user in terface for DRACULAE and patiently listening to me work out many of the ideas in this thesis at the white b oard for the record Nick named the parsing application DRACULA and I simply added an E Ken Whelan for his fantastic pro ofreading Gary Anderson who saved my life on many an o ccasion and was consistently an amazing source of information useful and entertaining Ho da Fahmy whose exp erience and insight have b een very helpful and who raised some of the semantics issues discussed at the end of this thesis Jianping Wu for his help with course work and things C Talib Hussein Laurie Ricker and all the other students and professors who help ed me survive my MSc in various ways Thanks to Genarro Costagliola who provided what for me was a highly inuential draft pap er on recognizing a subset of mathematics notation using a p ositional grammar and some valuable comments Thanks to Steve Smithies Kevin Novins and Jim Arvo for letting me use FFES to develop DRACULAE Without FFES it would have b een much harder to develop and test DRACULAE Thanks to Debby Linda Irene Sandra Tom Bradshaw and Gary Powley for all the help with the hard thankless stu that keeps things going and gets them done Finally thanks to Katey for listening to my rambles ab out this stu over and over again and for b eing a fabulous pro ofreader wife and friend Chapter Intro duction In certain domains the ability to use visual languages to communicate with computers allows simpler and more succinct expression of a users intentions than is p ossible with a string language For example it is easier for a user to draw a complicated mathematical expression in mathematics notation a visual language than to write the same expression in a string language A such as L T X or Maple Due to the sometimes more succinct and intuitive E expressions available in visual languages in comparison to string languages disciplines such as music architecture and engineering commonly use visual languages to convey information in the form of diagrams Informally a visual language is comprised of sets of symb ols laid out in two or three dimensions diagrams which comprise the set of legal expressions under a syntactic and semantic denition of the visual language The principal dierence b etween a visual language and a string language is that the syntax of a visual language is of higher dimensionality As a result b o okkeeping is required for spatial relationships b etween the terminals and nonterminals of the language when parsing A visual language may b e dened as in the following Graphical Primitive A graphical primitive is a line or dot in an image Symb ol A symb ol is dened by a nonempty set of spatial rela tions on graphical primitives which are intended to b e p er ceived as a single unit Diagram A diagram is a two or three dimensional collection set of symb ols Intro duction Visual Language A visual language is a set of diagrams which are considered valid expressions in the dened language Both the syntax and semantics of a visual language are de termined through the spatial relationships b etween symb ols in a diagram based on description in Currently there is ongoing research into the design and development of visual programming languages including compilercompilers for these lan guages The diagrams of a visual programming language may b e translated to an executable form using a visual language compiler Spread sheets are the most common and p erhaps also the most successful typ e of visual programming language to date Some visual languages such as math notation music notation and engi neering drawings are not formally dened and have dialects ie notational practices which are not standard but accepted We call these visual lan guages natural visual languages and dene them as in the following Dialect A dialect of a visual language is a variant of a visual language which employs symb olic andor spatial conventions which are not standard in all other variants of the visual language Natural Visual Language A natural visual language is a vi sual language for which no formal description exists andor dialects of the visual language exist Mathematics notation is the natural visual language on which we fo cus in this thesis For the remainder of this thesis when mathematics nota tion is used it is the Western standard read lefttoright which is b eing indicated for an interesting comparison see for a discussion of recogniz ing Arabic mathematical notation which is read righttoleft Mathematics notation is not formally dened we usually assume that mathematics no tation includes notations for algebra calculus logic and a numb er of other mathematical disciplines Mathematical discourse also often involves the use of dened notation and symb ols often have dierent semantic interpreta tions eg fb may b e interpreted as function application or as implied multiplication or may b e dened notation pro ducing dialects Natural visual languages such as mathematics notation have b oth hard and soft conventions Hard conventions are those asp ects of a visual lan guage that are consistent across dialects Soft conventions may or may not Intro duction apply to a particular diagram dep ending on the dialect used when a diagram was created An example of a hard convention in mathematics notation is the lefttoright direction of interpretation Soft conventions in mathematics notation include layout of symb ols The lack of formal denition for natural visual languages necessitates identication of hard and soft constraints b efore a language denition may b e constructed Without formal denitions it is dicult and p erhaps im p ossible to pro duce a completely accurate list of the two sets of conventions for any natural visual language as these have to b e obtained largely through observation and introsp ection Once the conventions of the language are felt to b e reasonably well understo o d and classied as hard or soft one may set ab out dening a syntax sp ecifying legal symb ol placement in diagrams and the semantic interpretation of this set of legal diagrams Again the lack of formal denition prevents this language denition from b eing complete in many cases In the case of mathematics notation some general descriptions of the structure of the notation are available from typ esetting pro cedures a history of the evolution of mathematics notation and a history of mathematics Ca joris history of notation unfortunately fo cuses on the evolution of symb ols rather than on the use of space b etween symb ols In addition the typ esetting references describ e notation from the p ersp ective of generating mathematics notation and do not explicitly state the hard and soft conventions of the notation Dialects complicate syntactic and semantic denition due to the resulting multiple interpretations that are p ossible For example in mathematics no tation it is imp ossible to determine whether cos should b e interpreted as a function or implied multiplication of variables without a prior decision ab out how to interpret this letter grouping ie cho osing a soft convention This is an example of how interpreting visual language dialects requires a priori information ab out the intended dialect of interpretation b efore the intended syntax and semantics can b e obtained from a diagram If this information is available the appropriate soft conventions may b e adopted to prop erly recognize a diagram Intro duction The Visual Language Recognition Pro cess Figure shows the pro cess of recognizing visual languages by computer and the four general typ es of diagram representation usable by a computer lab eled Typ es IIV Each of these diagrammatic representations is explained further b elow Visual Language Editor - Allows direct input of symbols TYPE II: Attributed Symbol List via mouse, data tablet and keyboard. TYPE IV: Semantic Representation Parsing TYPE I: Pixel Map Symbol Recognition a. Handwritten, Noisy Semantic Evaluation b. Handwritten, Noise-Free TYPE III:Structural Representation c. Typeset, Noisy * d. Typeset, Noise-Free
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